Assistant Vice President, Health Analytics Data Scientist

$160K - $190K Atlanta, GA, US Mid Level Data Scientist

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About This Role

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Posting Description: Aon is looking for an Assistant Vice President, Health Analytics Data Scientist

Chicago, IL; Atlanta, GA; Denver, CO; Seattle, WA are preferrable locations but open to other Aon locations or possibly remote for the right fit

We are seeking a hands-on, statistically grounded health data scientist, health actuary or medical economics expert to work on cost-of-care measurement, causal inference and predictive modeling workstreams and development of Aon analytics capabilities. You will own the design, validation, documentation, and delivery of the statistical and machine learning / predictive modeling algorithms and analyses that help employers and their partners understand cost, risk, and the impact of health programs – including total-cost-of-care predictive modeling and causal evaluations of programs and vendors using matched cohort trial emulation, difference-in-differences, and risk adjustment. You will work to evolve capabilities and model performance, review code and models, document and present results to clients and senior leaders, and take models from development into governed, monitored production, working closely with actuaries, consultants, clinical and innovation specialists, Aon Business Solutions / Technology (ABS), technology architects, and engineering teams.

The ideal candidate has 7+ years of experience including commercial health insurance and/or health benefits consulting, a strong foundation in applied statistics and causal inference, and a track record of building and deploying tabular predictive models on large medical and pharmacy claims datasets. The ability to document methods rigorously and present results clearly to technical and non-technical audiences is critical. Practical experience with MLOps and model governance, and with partnering with enterprise technology and distributed engineering teams to move models into scalable, supported production, is essential.

Aon is in the business of better decisions

At Aon, we shape decisions for the better to protect and enrich the lives of people around the world.

As an organization, we are united through trust as one inclusive team and we are passionate about helping our colleagues and clients succeed.

What the day will look like

  • Develop and deliver causal inference and predictive modeling workstreams for the US Health Analytics practice, ensuring methodological rigor while staying hands-on in building, reviewing, and validating models and code within project timelines
  • Collaborate closely with Aon Business Solutions / Technology (ABS), cloud architects, and offshore engineering and data science teams to align on data pipelines, model architecture, and deployment standards, coordinate delivery across locations and time zones, and move models from development into scalable, supported production
  • Design, build, and tune tabular analyses and predictive models including gradient-boosted, statistical methods and emerging AI modeling techniques– such as cost and utilization forecasting, high-cost claimant prediction, risk stratification, and program engagement propensity – on large eligibility, medical claims, and pharmacy claims datasets from multiple sources
  • Design and execute medical economics and causal inference studies that estimate the impact of programs, treatments, vendors, and point solutions on financial, utilization, and clinical outcomes, using matched cohort target trial emulation (explicit eligibility, time zero, and follow-up definitions; propensity score matching or weighting), difference-in-differences and event-study designs with parallel-trends and sensitivity testing, and risk adjustment
  • Document methods, assumptions, data definitions, and results thoroughly – methodology write-ups, technical appendices, model cards, and reproducible analysis records – and present findings, uncertainty, and limitations clearly to clients, carriers, consultants, and senior leaders through polished reports and presentations
  • Ensure MLOps and model governance end-to-end, including experiment tracking, model registries, reproducible training pipelines, automated testing, CI/CD, drift and performance monitoring, and model documentation, validation, and approval workflows aligned with Aon’s risk, privacy, and compliance standards
  • Serve as senior technical, code, and methodology reviewer for analytics deliverables, and set standards for model validation, explainability, bias and fairness review, and quality control
  • Partner with senior leaders across Aon’s health actuarial, consulting, clinical, innovation, and specialty practice areas, and stay current on emerging vendors and startups in healthcare navigation, digital health, and care delivery
  • Mentor and coach data scientists, and lead multiple projects with clear priorities, timelines, and milestones

Skills and experience that will lead to success

  • Minimum 7 years of total experience, including health insurance, with a track record of delivering value to clients and colleagues
  • Strong foundation in applied statistics or medical economics, including model training and validation, statistical testing, regression and generalized linear models, and experimental and quasi-experimental design
  • Deep hands-on experience building, tuning, and interpreting tabular predictive models, including feature engineering, cross-validation, hyperparameter optimization, calibration, class imbalance, and explainability methods
  • Hands-on experience applying causal inference to observational health data / medical economics, including matched cohort target trial emulation, propensity score methods, difference-in-differences (including staggered-adoption and event-study approaches), sensitivity analysis, and risk adjustment, with financial, utilization, and clinical outcomes metrics
  • Detailed understanding of US health data, including eligibility, medical and pharmacy claims, and coding standards such as ICD-10-CM, ICD-10-PCS, CPT, DRG, and related industry standards
  • Experience with MLOps and model governance – such as MLflow or similar tooling, model registries, automated training and scoring pipelines, version control, drift and performance monitoring, and model risk documentation and review
  • Experience partnering with enterprise technology and architecture teams and working with offshore or distributed engineering teams to deploy and support models in production
  • Expert Python and/or R and strong SQL; Databricks, Spark, Azure, Docker, and CI/CD experience a plus
  • High-performing individual contributor who can mentor junior data scientists and work independently across multiple projects
  • Excellent written and verbal communication, with a track record of documenting methods and assumptions clearly and presenting statistical results, uncertainty, and limitations to actuaries, consultants, clients, and non-technical audiences

Education:

Bachelor’s or master’s degree in statistics, biostatistics, mathematics, economics, health services / public health research, data science, computer science, actuarial science, or a related quantitative field, or equivalent professional experience; advanced degrees are a plus

For positions in San Francisco and Los Angeles, we will consider for employment qualified applicants with arrest and conviction record in accordance with local Fair Chance ordinances. Aon is not accepting unsolicited resumes from search firms for this position. If you are a search firm, you will not be compensated in any way for your submission of a candidate, even if Aon hires that candidate.

Nothing in this job description restricts management's right to assign or reassign duties and responsibilities to this job at any time.

Pay Transparency Laws:

The salary range for this position (intended for U.S. applicants) is $160,000 to $190,000 annually. The actual salary will vary based on applicant’s education, experience, skills, and abilities, as well as internal equity and alignment with market data. The salary may also be adjusted based on applicant’s geographic location.

This position is eligible to participate in one of Aon’s annual incentive plans to receive an annual discretionary bonus in addition to base salary. The amount of any bonus varies and is subject to the terms and conditions of the applicable incentive plan. A summary of all the benefits offered for this position:

Aon offers a comprehensive package of benefits for full-time and regular part-time colleagues, including, but not limited to: a 401(k) savings plan with employer contributions; an employee stock purchase plan; consideration for long-term incentive awards at Aon’s discretion; medical, dental and vision insurance, various types of leaves of absence, paid time off, including 12 paid holidays throughout the calendar year, 15 days of paid vacation per year, paid sick leave as provided under state and local paid sick leave laws, short-term disability and optional long-term disability, health savings account, health care and dependent care reimbursement accounts, employee and dependent life insurance and supplemental life and AD&D insurance; optional personal insurance policies, adoption assistance, tuition assistance, commuter benefits, and an employee assistance program that includes free counseling sessions. Eligibility for benefits is governed by the applicable plan documents and policies.

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Salary Context

This $160K-$190K range is above the median for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).

View full Data Scientist salary data →

Role Details

Company Aon
Title Assistant Vice President, Health Analytics Data Scientist
Location Atlanta, GA, US
Category Data Scientist
Experience Mid Level
Salary $160K - $190K
Remote No

About This Role

Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'

Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.

Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Aon, this role fits into their broader AI and engineering organization.

Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.

What the Work Looks Like

A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.

Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.

Skills Required

Azure (8% of roles) Docker (4% of roles) Mlflow (1% of roles) Python (18% of roles)

Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.

Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.

Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.

Compensation Benchmarks

Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. This role's midpoint ($175K) sits 8% below the category median. Disclosed range: $160K to $190K.

Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.

Aon AI Hiring

Aon has 1 open AI role right now. They're hiring across Data Scientist. Based in Atlanta, GA, US. Compensation range: $190K - $190K.

Location Context

Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).

Career Path

Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.

From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.

Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.

What to Expect in Interviews

Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.

When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.

AI Hiring Overview

The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.

The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).

Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.

The AI Job Market Today

The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 1,503 roles with disclosed compensation, the median salary for Data Scientist positions is $190,000. Actual compensation varies by seniority, location, and company stage.
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
About 6% of the 9,079 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Aon is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Data Scientist positions include Senior Data Scientist, ML Engineer, AI Product Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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